Personalized Alignment Revisited: The Necessity and Sufficiency of User Diversity
Personalized alignment aims to adapt large language models to heterogeneous user preferences, yet the precise theoretical conditions for its statistical efficiency have not been formally established. This paper characterizes the conditions under which personalized alignment achieves O(1) online regret and log(1/epsilon) offline sample complexity. We show that these optimal rates depend on a specific user-diversity condition: the population of user-specific heads must span the latent reward direc
Record details
Published: 9 May 2026
Source: arXiv
Category: Research
Topics: Safety & alignment
Retrieved: 14 July 2026
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ethics.ai (9 May 2026), “Personalized Alignment Revisited: The Necessity and Sufficiency of User Diversity,” evidence record 4654, https://ethics.ai/record/4654 (originally published by arXiv).
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